the information the cluster gave us about the features X. With regression trees, what we want to do is maximize I[C;Y], where Y is now the dependent variable, and C are now is the variable saying which leaf of the tree we end up at. Once again, we can’t do a direct maximization, so we again do a greedy search. We
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importance_type (string, optional (default=’split’)) – The type of feature importance to be filled into feature_importances_. If ‘split’, result contains numbers of times the feature is used in a model. If ‘gain’, result contains total gains of splits which use the feature. **kwargs – Other parameters for the model.
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Distributed computing. In distributed computing, data and tasks are split across many connected computers, often reducing execution times. Make sure you are using a distributed environment that’s well.
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Decision Trees. Classification and Regression Trees or CART for short is a term introduced by Leo Breiman to refer to Decision Tree algorithms that can be used for classification or regression predictive modeling problems.. Classically, this algorithm is referred to as "decision trees", but on some platforms like R they are referred to by the more modern term CART.